Abstract
The paper describes a simple instrumental variable method for identifying the structure of a wide class of time-series models. The method is aimed at providing a parametrically efficient (parsimonious) model structure which will lead to a combination of low residual error variance, i.e. a good explanation of the data, and low parametric estimation error variance (as measured by some norm associated with the covariance matrix of the estimation errors). It can be applied to single input-single output and multivariable systems using either discrete or continuous-time series models. It can also function as a recursive (on-line) test for reduction in model order.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
